Question

DRAWING CONCLUSIONS A company wants to test whether a nutritional supplement has an adverse effect on an athlete’s heart rate while exercising. Identify a potential problem, if any, with each experimental design. Then describe how you can improve it. (See Example 3.) a. The company randomly selects 250 athletes. Half of the athletes receive the supplement and their heart rates are monitored while they run on a treadmill. The other half of the athletes are given a placebo and their heart rates are monitored while they lift weights. The heart rates of the athletes who took the supplement signifi cantly increased while exercising. b. The company selects 1000 athletes. The athletes are divided into two groups based on age. Within each age group, the athletes are randomly assigned to receive the supplement or the placebo. The athletes’ heart rates are monitored while they run on a treadmill. There was no signifi cant difference in the increases in heart rates between the two groups.

Short answer

In scenario a, the type of exercise performed by each group must be the same to avoid introducing a confounding variable. In scenario b, ensure equal distribution of the supplement and placebo within every age group to prevent age-related biases. Controlling additional factors such as gender, fitness level, and exercise type would enhance the quality of the experimental designs in both cases.

Step-by-step solution

Identify Problems in Scenario a

In scenario a, the issue lies in the mismatch of exercises performed by the two groups. One group was running on a treadmill, while the other was lifting weights. These are two distinct types of physical exertion and will affect the heartbeat rate differently. Therefore, even if the heartbeat rates of the supplement group significantly increased while exercising compared to the placebo group, it would not be possible to conclude that the supplement was the cause.

Improve Scenario a

To correct the bias in scenario a, ensure that both groups perform the same exercise. This way, the changes in heart rate between the two groups could be attributed to the supplement or the placebo rather than the difference in exercises. Additionally, taking other factors into account, such as athletes' ages, genders, and fitness levels, could further improve the experimental design.

Identify Problems in Scenario b

In scenario b, athletes were divided into two groups based on their age, but it's not clearly stated whether each age group contains equal numbers of athletes taking the supplement and the placebo. If not, any observed differences in heart-rate increase could be related to the supplement, the placebo, or simply age.

Improve Scenario b

To improve the experimental setup of Scenario b, ensure the age groups are evenly split between the supplement and placebo. In addition to adjusting for the age in each group, controlling other features such as gender, fitness level, and type of exercise could help make the experimental design more robust and the conclusion more accurate.

Key concepts

Control Group

In research, a control group is essential to draw valid conclusions. The control group acts as a baseline to compare the effects of an experimental treatment, like a drug or a supplement. This group does not receive the treatment being tested. Instead, they might receive a placebo, which is a harmless, inactive substance designed to mimic the treatment.

By observing the control group, researchers can determine whether changes observed in the experimental group are actually due to the treatment or if they are random or related to other factors. For example, if athletes taking a nutritional supplement show an increase in heart rate, and the control group does not, we can be more confident that the supplement is influencing the outcome.

A well-designed experiment should carefully define and maintain a control group to ensure results are accurately measured. Without a control group, it's challenging to establish a cause-and-effect relationship.

Placebo Effect

The placebo effect is a fascinating phenomenon in experiments that can skew results if not managed properly. It occurs when participants experience changes in their condition simply because they believe they are receiving a treatment, even if it's inactive or a placebo. This psychological effect can lead to real changes in health markers or symptoms.

In the context of our exercise, giving the placebo to a portion of the participants helps scientists to understand whether the effects observed in the experimental group are due to the supplement itself or from the participants' expectations about the treatment. When designing an experiment, it's crucial to account for the placebo effect to avoid falsely attributing effects to the treatment that are actually psychological.

By comparing the outcomes of participants receiving the actual supplement with those receiving the placebo, researchers can differentiate between actual physiological effects and those related to participant expectations.

Bias in Experiments

Bias in experiments can significantly affect the validity of research findings. Bias arises when certain factors influence the outcomes of a study more than the factors being tested, such as the treatment itself. It's important to identify and control for bias to maintain the integrity of the experiment.

One common type of bias occurs when there are differences in the way groups are treated or in the conditions they experience. In scenario a of our exercise, bias was introduced due to the two groups performing different types of exercise. This led to skewed results and made it difficult to attribute changes in heart rate solely to the supplement.

To reduce bias, experimenters should ensure that all participants experience equal conditions, except for the treatment being tested. They should also consider other factors like age, gender, and fitness level to avoid unintentional biases that could influence the results.

Random Selection

Random selection is a cornerstone of experimental design. It ensures that every participant has an equal chance of being assigned to any given group, whether it be the control or experimental group. This randomness helps control for variables and reduces bias, making the results more generalizable to the larger population.

In experimental setups like our exercise, random selection helps ensure that each group - those receiving the supplement and those taking the placebo - are comparable in terms of characteristics and behaviors. This can include age, fitness level, and gender.

Proper random selection means that any observed effects are more likely due to the treatment than to other extraneous factors. Experiments lacking random selection may produce biased results that do not accurately represent the true impact of the treatment in question.

Variables in Experiments

Variables play a crucial role in experimental design, acting as the parts of an experiment that researchers manipulate and measure. These include independent variables, which researchers change to observe their effects, and dependent variables, which are the outcomes of those changes.

For our specific scenarios, the nutritional supplement is the independent variable, while the athletes' heart rates are the dependent variable. Controlling other variables, like the type of exercise, is key to obtaining reliable results.

Well-controlled experiments manage these variables by keeping conditions similar across all groups except for the independent variable. When multiple variables change at once, it becomes difficult to determine which variable is causing the observed outcome. Keeping a tight control on variables helps ensure that the results of the experiment accurately reflect the relationships between these elements.